def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
'weight' - the number of times a feature is used to split the data across all trees.
'gain' - the average gain of the feature when it is used in trees
'cover' - the average coverage of the feature when it is used in trees
fmap: str (optional)
The name of feature map file
- weight 在tree中用到的次数计数
- gain 在tree中用到时的gain之和/在tree中用到的次数计数
def feature_importance(self, importance_type='split'):
Get feature importances
importance_type : str, default "split"
How the importance is calculated: "split" or "gain"
"split" is the number of times a feature is used in a model
"gain" is the total gain of splits which use the feature
result : array
Array of feature importances.
1. Regular feature importance
- $feature_total_importance_j$ is the individual feature importance of the j-th feature.
- $average_feature_importance$ is the average feature importance of the j-th feature in the i-th combinational feature.
2. Internal feature importance